Update app.py

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 import numpy as np
 import random
-import spaces  # [uncomment to use ZeroGPU]
+import spaces  # Uncomment if you're using ZeroGPU
 from diffusers import DiffusionPipeline
 import torch
 from tags import participant_tags, tribe_tags, role_tags, skin_tone_tags, body_type_tags, tattoo_tags, piercing_tags, expression_tags, eye_tags, hair_style_tags, position_tags, fetish_tags, location_tags, camera_tags, atmosphere_tags
@@ -15,25 +15,25 @@ else:
     torch_dtype = torch.float32
 
 pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
+pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
-@spaces.GPU  # [uncomment to use ZeroGPU]
-def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
-          selected_participant_tags, selected_tribe_tags, selected_role_tags, selected_skin_tone_tags, selected_body_type_tags,
-          selected_tattoo_tags, selected_piercing_tags, selected_expression_tags, selected_eye_tags,
-          selected_hair_style_tags, selected_position_tags, selected_fetish_tags, selected_location_tags,
-          selected_camera_tags, selected_atmosphere_tags, active_tab, progress=gr.Progress(track_tqdm=True)):
 
+@spaces.GPU  # Uncomment if using ZeroGPU
+def infer(
+    prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,
+    selected_participant_tags, selected_tribe_tags, selected_role_tags, selected_skin_tone_tags, selected_body_type_tags,
+    selected_tattoo_tags, selected_piercing_tags, selected_expression_tags, selected_eye_tags, selected_hair_style_tags,
+    selected_position_tags, selected_fetish_tags, selected_location_tags, selected_camera_tags, selected_atmosphere_tags,
+    active_tab, progress=gr.Progress(track_tqdm=True)
+):
+    # Handle the active tab and generate the prompt accordingly
     if active_tab == "Prompt Input":
-        # Use the user-provided prompt
-        final_prompt = f'score_9, score_8_up, score_7_up, source_anime, {prompt}'
-    
-    elif active_tab == "Straight" :
-        # Use tags from the "Gay" tab
-        selected_tags = (
+        final_prompt = f"score_9, score_8_up, score_7_up, source_anime, {prompt}"
+    else:
+        tag_list = (
             [participant_tags[tag] for tag in selected_participant_tags] +
             [tribe_tags[tag] for tag in selected_tribe_tags] +
             [role_tags[tag] for tag in selected_role_tags] +
@@ -50,63 +50,16 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
             [camera_tags[tag] for tag in selected_camera_tags] +
             [atmosphere_tags[tag] for tag in selected_atmosphere_tags]
         )
-        tags_text = ', '.join(selected_tags)
-        final_prompt = f'score_9, score_8_up, score_7_up, source_anime, {tags_text}'
+        final_prompt = f"score_9, score_8_up, score_7_up, source_anime, {', '.join(tag_list)}"
 
-    elif active_tab == "Gay" :
-        # Use tags from the "Gay" tab
-        selected_tags = (
-            [participant_tags[tag] for tag in selected_participant_tags] +
-            [tribe_tags[tag] for tag in selected_tribe_tags] +
-            [role_tags[tag] for tag in selected_role_tags] +
-            [skin_tone_tags[tag] for tag in selected_skin_tone_tags] +
-            [body_type_tags[tag] for tag in selected_body_type_tags] +
-            [tattoo_tags[tag] for tag in selected_tattoo_tags] +
-            [piercing_tags[tag] for tag in selected_piercing_tags] +
-            [expression_tags[tag] for tag in selected_expression_tags] +
-            [eye_tags[tag] for tag in selected_eye_tags] +
-            [hair_style_tags[tag] for tag in selected_hair_style_tags] +
-            [position_tags[tag] for tag in selected_position_tags] +
-            [fetish_tags[tag] for tag in selected_fetish_tags] +
-            [location_tags[tag] for tag in selected_location_tags] +
-            [camera_tags[tag] for tag in selected_camera_tags] +
-            [atmosphere_tags[tag] for tag in selected_atmosphere_tags]
-        )
-        tags_text = ', '.join(selected_tags)
-        final_prompt = f'score_9, score_8_up, score_7_up, source_anime, {tags_text}'
-        
-    elif active_tab == "Lesbian" :
-        # Use tags from the "Lesbien" tab
-        selected_tags = (
-            [participant_tags[tag] for tag in selected_participant_tags] +
-            [tribe_tags[tag] for tag in selected_tribe_tags] +
-            [role_tags[tag] for tag in selected_role_tags] +
-            [skin_tone_tags[tag] for tag in selected_skin_tone_tags] +
-            [body_type_tags[tag] for tag in selected_body_type_tags] +
-            [tattoo_tags[tag] for tag in selected_tattoo_tags] +
-            [piercing_tags[tag] for tag in selected_piercing_tags] +
-            [expression_tags[tag] for tag in selected_expression_tags] +
-            [eye_tags[tag] for tag in selected_eye_tags] +
-            [hair_style_tags[tag] for tag in selected_hair_style_tags] +
-            [position_tags[tag] for tag in selected_position_tags] +
-            [fetish_tags[tag] for tag in selected_fetish_tags] +
-            [location_tags[tag] for tag in selected_location_tags] +
-            [camera_tags[tag] for tag in selected_camera_tags] +
-            [atmosphere_tags[tag] for tag in selected_atmosphere_tags]
-        )
-        tags_text = ', '.join(selected_tags)
-        final_prompt = f'score_9, score_8_up, score_7_up, source_anime, {tags_text}'
-    
-    # Concatenate user-provided negative prompt with additional restrictions
+    # Concatenate additional negative prompts
     additional_negatives = "worst quality, bad quality, jpeg artifacts, source_cartoon, 3d, (censor), monochrome, blurry, lowres, watermark"
     full_negative_prompt = f"{additional_negatives}, {negative_prompt}"
 
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
+    generator = torch.Generator(device=device).manual_seed(seed)
 
-    generator = torch.Generator().manual_seed(seed)
-
-    # Generate the image with the final prompts
     image = pipe(
         prompt=final_prompt,
         negative_prompt=full_negative_prompt,
@@ -117,54 +70,32 @@ def infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance
         generator=generator
     ).images[0]
 
-    # Return image, seed, and the used prompts
-    return image, seed, f"Prompt used: {final_prompt}\nNegative prompt used: {full_negative_prompt}"
-
+    return image, seed, f"Prompt: {final_prompt}\nNegative Prompt: {full_negative_prompt}"
 
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
 
 css = """
 #col-container {
     margin: 0 auto;
     max-width: 640px;
 }
-#run-button {
-    width: 100%;
-}
 """
 
 with gr.Blocks(css=css) as demo:
-
     with gr.Column(elem_id="col-container"):
-        gr.Markdown("""# Rainbow Media X""")
+        gr.Markdown("# Image Generator with Tags and Prompts")
 
-        # Display result image at the top
         result = gr.Image(label="Result", show_label=False)
-
-        # Add a textbox to display the prompts used for generation
         prompt_info = gr.Textbox(label="Prompts Used", lines=3, interactive=False)
-
-        # State to track active tab
         active_tab = gr.State("Prompt Input")
 
-        # Tabbed interface to select either Prompt or Tags
         with gr.Tabs() as tabs:
-            with gr.TabItem("Prompt Input") as prompt_tab:
-                prompt = gr.Textbox(
-                    label="Prompt",
-                    show_label=False,
-                    max_lines=1,
-                    placeholder="Enter your prompt",
-                    container=False,
-                )
-                prompt_tab.select(lambda: "Prompt Input", inputs=None, outputs=active_tab)
+            # Prompt Input Tab
+            with gr.TabItem("Prompt Input"):
+                prompt = gr.Textbox(label="Prompt", placeholder="Enter your custom prompt")
+                tabs.select(lambda: "Prompt Input", inputs=None, outputs=active_tab)
 
-            with gr.TabItem("Straight") as straight_tag_tab:
-                # Tag selection checkboxes for each tag group
+            # Straight Tab
+            with gr.TabItem("Straight"):
                 selected_participant_tags = gr.CheckboxGroup(choices=list(participant_tags.keys()), label="Participant Tags")
                 selected_tribe_tags = gr.CheckboxGroup(choices=list(tribe_tags.keys()), label="Tribe Tags")
                 selected_role_tags = gr.CheckboxGroup(choices=list(role_tags.keys()), label="Role Tags")
@@ -180,11 +111,10 @@ with gr.Blocks(css=css) as demo:
                 selected_location_tags = gr.CheckboxGroup(choices=list(location_tags.keys()), label="Location Tags")
                 selected_camera_tags = gr.CheckboxGroup(choices=list(camera_tags.keys()), label="Camera Tags")
                 selected_atmosphere_tags = gr.CheckboxGroup(choices=list(atmosphere_tags.keys()), label="Atmosphere Tags")
-                straight_tag_tab.select(lambda: "Straight", inputs=None, outputs=active_tab)
-                
-            
-            with gr.TabItem("Gay") as gay_tag_tab:
-                # Tag selection checkboxes for each tag group
+                tabs.select(lambda: "Straight", inputs=None, outputs=active_tab)
+
+            # Gay Tab
+            with gr.TabItem("Gay"):
                 selected_participant_tags = gr.CheckboxGroup(choices=list(participant_tags.keys()), label="Participant Tags")
                 selected_tribe_tags = gr.CheckboxGroup(choices=list(tribe_tags.keys()), label="Tribe Tags")
                 selected_role_tags = gr.CheckboxGroup(choices=list(role_tags.keys()), label="Role Tags")
@@ -200,10 +130,10 @@ with gr.Blocks(css=css) as demo:
                 selected_location_tags = gr.CheckboxGroup(choices=list(location_tags.keys()), label="Location Tags")
                 selected_camera_tags = gr.CheckboxGroup(choices=list(camera_tags.keys()), label="Camera Tags")
                 selected_atmosphere_tags = gr.CheckboxGroup(choices=list(atmosphere_tags.keys()), label="Atmosphere Tags")
-                gay_tag_tab.select(lambda: "Gay", inputs=None, outputs=active_tab)
+                tabs.select(lambda: "Gay", inputs=None, outputs=active_tab)
 
-            with gr.TabItem("Lesbian") as lesbian_tag_tab:
-                # Tag selection checkboxes for each tag group
+            # Lesbian Tab
+            with gr.TabItem("Lesbian"):
                 selected_participant_tags = gr.CheckboxGroup(choices=list(participant_tags.keys()), label="Participant Tags")
                 selected_tribe_tags = gr.CheckboxGroup(choices=list(tribe_tags.keys()), label="Tribe Tags")
                 selected_role_tags = gr.CheckboxGroup(choices=list(role_tags.keys()), label="Role Tags")
@@ -219,68 +149,23 @@ with gr.Blocks(css=css) as demo:
                 selected_location_tags = gr.CheckboxGroup(choices=list(location_tags.keys()), label="Location Tags")
                 selected_camera_tags = gr.CheckboxGroup(choices=list(camera_tags.keys()), label="Camera Tags")
                 selected_atmosphere_tags = gr.CheckboxGroup(choices=list(atmosphere_tags.keys()), label="Atmosphere Tags")
-                lesbian_tag_tab.select(lambda: "Lesbian", inputs=None, outputs=active_tab)
-
-        # Full-width "Run" button
-        run_button = gr.Button("Run", scale=0, elem_id="run-button")
+                tabs.select(lambda: "Lesbian", inputs=None, outputs=active_tab)
 
+        # Advanced Settings
         with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Textbox(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=True,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+            negative_prompt = gr.Textbox(label="Negative Prompt", placeholder="Enter negative prompt")
+            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+            randomize_seed = gr.Checkbox(label="Randomize Seed", value=True)
 
             with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,
-                )
+                width = gr.Slider(label="Width", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
+                height = gr.Slider(label="Height", minimum=256, maximum=MAX_IMAGE_SIZE, step=32, value=1024)
 
             with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=7,
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=35,
-                )
-
-        gr.Examples(
-            examples=examples,
-            inputs=[prompt]
-        )
+                guidance_scale = gr.Slider(label="Guidance Scale", minimum=0.0, maximum=10.0, step=0.1, value=7)
+                num_inference_steps = gr.Slider(label="Number of Inference Steps", minimum=1, maximum=50, step=1, value=35)
 
+        run_button = gr.Button("Run")
         run_button.click(
             infer,
             inputs=[prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps,